Data fusion technique to compute reservoir quality and completion quality by combining various log measurements

ABSTRACT

Methods may include normalizing two or more wellbore logs obtained from the output of two or more wellbore tool surveys of a wellbore in a formation of interest; inputting two or more wellbore logs into a correlation matrix; assigning each of the two or more wellbore logs a positive or negative value based on the impact on a selected wellbore quality; performing a principal component analysis of the two or more wellbore logs to obtain one or more loading vectors; computing weighting factors for each of the two or more wellbore logs from the one or more loading vectors; and generating a quality index by linearly combining the two or more wellbore logs using the computed weighting factors.

BACKGROUND

Identification of regions in a formation that contain hydrocarbons isone of the primary goals of oil and gas exploration. One way to identifythe hydrocarbon reservoirs within a subterranean formation, alsoreferred to as hydrocarbon pay, is from differential responses ofvarious logging tools along a wellbore. Wellbore tools may also identifyother qualities of interest such as rock composition, anisotropicstructures, water saturation, and other features that can aid wellboreoperations such as completions, stimulation, production, and the like.Common logging tools may include electrical tools, electromagnetictools, acoustic tools, nuclear tools, and nuclear magnetic resonance(NMR) tools, and a number of others.

As the number of wellbore tools used to study a formation increases,more data is generated that must be sorted and analyzed, which canimpart additional time and expense in order to evaluate the informationfrom each tool. In addition to sheer data volume, individual tools maybe more or less susceptible to error and methods are needed to identifyand correct for data redundancy across multiple wellbore logs in orderto efficiently identify wellbore zones that may contain economicallyviable pay and potential completion zones.

SUMMARY

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

In one aspect, embodiments disclosed herein relate to methods that mayinclude normalizing two or more wellbore logs obtained from the outputof two or more wellbore tool surveys of a wellbore in a formation ofinterest; inputting two or more wellbore logs into a correlation matrix;assigning each of the two or more wellbore logs a positive or negativevalue based on the impact on a selected wellbore quality; performing aprincipal component analysis of the two or more wellbore logs to obtainone or more loading vectors; computing weighting factors for each of thetwo or more wellbore logs from the one or more loading vectors; andgenerating a quality index by linearly combining the two or morewellbore logs using the computed weighting factors.

In another aspect, embodiments disclosed herein relate to methods thatmay include normalizing two or more wellbore logs obtained from theoutput of two or more wellbore tool surveys of a wellbore in a formationof interest; inputting two or more wellbore logs into a correlationmatrix; assigning each of the two or more wellbore logs a positive ornegative sign based on the impact on a selected wellbore quality;performing a principal component analysis of the two or more wellborelogs to obtain one or more loading vectors; computing weighting factorsfor each of the two or more wellbore logs from the one or more loadingvectors; generating a reservoir quality log by linearly combining thetwo or more wellbore logs using the computed weighting factors;generating a completion quality log by linearly combining the two ormore wellbore logs using the computed weighting factors; and preparing acomposite reservoir quality index from the product of the reservoirquality log and the completion quality log.

Other aspects and advantages of the claimed subject matter will beapparent from the following description and the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a flow diagram depicting a method in accordance withembodiments of the present disclosure;

FIG. 2 is an illustration depicting an example of a pair-wisecorrelation matrix used to analyze wellbore measurements in accordancewith embodiments of the present disclosure. The bar on the rightindicates the level of positive or negative correlation betweenmeasurement pairs;

FIG. 3.1 is a correlation map for a number of logs in accordance withembodiments of the present disclosure;

FIG. 3.2 is a correlation matrix for a number of logs that are used asexample input for reservoir quality (RQ) in accordance with embodimentsof the present disclosure;

FIG. 3.3 is a graphical representation of the first three principalcomponents for a number of logs that are used as example input inaccordance with embodiments of the present disclosure;

FIGS. 4.1 and 4.2 are graphical representations depicting the projectionof data measurements and loading vectors in principal component spacerepresented in two dimensions and three dimensions, respectively, inaccordance with embodiments of the present disclosure;

FIG. 5 is a graphical representation showing a continuous RQ indexgenerated by linearly combining four well log measurements in accordancewith embodiments of the present disclosure;

FIG. 6 is a graphical representation showing a continuous RQ index withappended pay flags generated by linearly combining four well logmeasurements with weighting factors applied and followed by applying anoptimal thresholding value in accordance with embodiments of the presentdisclosure;

FIG. 7.1 is a correlation map for a number of logs that are used asexample input for completion quality (CQ) index computation inaccordance with embodiments of the present disclosure;

FIG. 7.2 is a correlation matrix for a number of logs as example inputin accordance with embodiments of the present disclosure;

FIG. 7.3 is a graphical representation of the first three principalcomponents for a number of logs as example input in accordance withembodiments of the present disclosure;

FIGS. 8.1 and 8.2 are graphical representations depicting projection ofdata measurements in principal component space represented in twodimensions and three dimensions, respectively, in accordance withembodiments of the present disclosure;

FIG. 9 is a graphical representation showing a continuous CQ indexgenerated by linearly combining four example well log measurements inaccordance with embodiments of the present disclosure; and

FIG. 10 is a schematic showing an example of a computer system forexecuting methods in accordance with the present disclosure.

DETAILED DESCRIPTION

In one aspect, embodiments in accordance with the present disclosure aredirected to a data analytic approach to compute a continuous qualityindex from a linear combination of available log measurements from aninterval of interest in a wellbore traversing a subterranean formationthat accounts for data redundancy across multiple information sources.In another aspect, embodiments disclosed herein relate to data fusiontechniques that combine multiple wellbore log measurements and/or otherdigital data to compute wellbore quality indices automatically. In oneor more embodiments, wellbore quality indices may include continuousreservoir quality (RQ), completion quality (CQ), and composite qualityindices (QI), and the like.

Methods in accordance with the present disclosure may be used toquantify, improve, and automate computation of reservoir quality andcompletion quality evaluation, interpretation, and mapping by combiningmultiple log measurements. Previous approaches have used arbitrarybinary threshold cutoffs of independent wellbore measurements to computeflags for specified wellbore qualities (often reservoir quality) thatmay be applied to other wellbore log measurements over the sameinterval. However, approaches that generate binary flags from a seriesof single source of wellbore information are susceptible to thepropagation of errors from individual wellbore logs, which can lead tofewer identified intervals of interest and less certainty of specifiedquality within the flagged interval. The major limitation of applyingbinary thresholds to individual wellbore logs is that intervals withinthe wellbores are labeled as “good” or “bad” quality and then the “good”intervals are flagged if they are “good” across all logs. This leads tosmaller and fewer “good” reservoir intervals as more measurements becomeavailable, which is counterintuitive and unreasonable for reservoirinterpretation.

In one or more embodiments, methods may include generating one or morecontinuous wellbore quality indices by assigning optimal weights to agrouping of wellbore measurements for an interval or reservoir ofinterest, such that highly correlated logs share weighting factors,while more independent logs are assigned higher weighting factors. Thismethod is contrasted with prior approaches to evaluate RQ and CQ thatapply binary threshold cutoffs to logs to denote “good” or “bad”qualities without taking log dependence/redundancy for multiple logmeasurements into account.

With respect to FIG. 1 a general flow diagram is shown for a method togenerate continuous wellbore quality indices through linear combinationof available log measurements and/or other digital data, which utilizesweighting factors to minimize data redundancy. Beginning at 102,wellbore measurements are collected from available wellbore logs fromany number of wellbore tools and other digital data. At 104, thewellbore measurements are input into a correlation matrix duringexploratory data analysis (EDA). The correlation matrix is establishedby organizing available wellbore data using a diagnostic tool thatcomputes and displays a color-coded pair-wise matrix to aid userclassification of the available wellbore measurements into groupingsthat provide information for a given wellbore quality being studied. Theestablished correlation matrix may provide users with an intuitivevisualization that allows the separation of available wellboremeasurements into distinct groups for subsequent analysis based on thedegree of cross correlation for subsequent analysis. In someembodiments, wellbore logs may be grouped based on the criteria ofminimizing variation within individual well log groups, while maximizingthe variation across different groups.

At 106, wellbore measurements may be grouped according to user-definedcriteria to generate quality indices that are tailored to a particularapplication, such as RQ and/or CQ. During EDA, wellbore logs areassigned positive “+” and “−” negative signs depending on whether theycontribute to the wellbore quality of interest positively or negatively.General petrophysical understanding and local knowledge about thestudied reservoir may also help the determination of the signs. In someembodiments, the correlation matrix may prompt users to determine signselection in sequence. For example, a user may be prompted to assignsigns to sequential logs based on the order of confidence in theaccuracy of the log data; the sequential logs may then be assigned apositive or negative sign dependent on the degree of correlation to thefirst log. Positive correlations suggest that the compared wellbore logshave the same sign, while negative signs may be applied to negativecorrelations.

The grouped wellbore measurements are then processed by principalcomponent analysis (PCA) at 108. In one or more embodiments, wellborelogs that have been assigned signs may then be processed by PCA todetermine the principal components (PC) and corresponding loadingvectors for the data set. PCA transforms the wellbore data from theoriginal axes to principal axes. The first principal axis is thedirection in which the data are primarily distributed, or the “long”axis of the distribution in n-dimensional space.

In one or more embodiments, the number of principal components may bevaried. In some embodiments, a PCA may retain the first three PCs, andthe respective loading vectors may be displayed to show the redundancyamong the logs. PCA results may be plotted in 2D or 3D in PC space. Theplotted data may be used to generate loading vectors for the inputwellbore measurements, which are used for data dimension reductionand/or characterization of variance. While embodiments of the presentdisclosure are discussed using linear PCA, there are other methods thatcould be substituted for linear PCA, including, but not limited to,non-linear or kernel PCA, and similar other dimension reduction tools.

Following PCA, estimation tools such as the Kriging estimator may beapplied in PC loading space to measures the redundancy of the logs andcompute weighting factors at 110. Kriging estimators are known from useas a fundamental estimation tool in geostatistics, and is described, forexample, in E. H. Isaaks and R. M. Srivastava, 1989, An Introduction toApplied Geostatistics, Oxford University Press. In one or moreembodiments, a Kriging estimator is used and applied to the loadingvectors of log measurements using a covariance model with spatialanisotropy determined by the variance of each individual PC identified,which is then used to generate weighting factors for all available logs.

After weighting factors for each of the wellbore logs are calculated,the logs may be combined linearly to generate the continuous wellborequality index at 112, such as an RQ log or CQ log. In one or moreembodiments, users can apply a threshold cutoff at 114 to a generatedwellbore quality index to designate intervals having specifiedcharacteristics, such as indicating the presence of pay zones orintervals suitable as completion targets.

Methods in accordance with the present disclosure may be automated insome embodiments using supervised learning (machine learning) fromprevious log results. For example, if a user wants to employ acontinuous quality index resulting from the analysis of multiple welllogs to interpret various intervals of interest such as pay zones orcompletion targets, an interactive method may be employed to assist theoptimization of a threshold for a given quality index. Automated methodsmay be trained by setting a user-defined optimal threshold value for awellbore quality such as RQ, and the target intervals may be computed.Thus, a binary solution can be created from one or two quality indices,while retaining the continuous estimates of wellbore quality (RQ and CQ,for example).

If a flagged interval of interest is known and a well log already existsin a well, the threshold values used to flag the interval may be usedfor machine learning, which may minimize the misclassification rate byan iterative process with user input or automatically optimizing thethreshold value for generated quality indices. The misclassificationrate is the ratio between the number of data points that are classifiedincorrectly when comparing with the known pay flagged values and thetotal number of samples in the studied zone. In one or more embodiments,optimized threshold values computed in the training well containing aflagged interval of interest may be applied to neighboring wells orwells in similar formations.

EXAMPLES

The following examples are presented to illustrate the overall method ofgenerating a wellbore quality index for a number of wellbore qualities,and should not be construed to limit the scope of the disclosure, unlessotherwise expressly indicated in the appended claims. Even though themethodology is explained by logs in vertical wells, it can be applied tologs or measurements from wells in any orientation. The method can besimilarly applied to the analysis of real-time drilling measurement datawhen the data are acquired in sequential time frames.

Example 1: Reservoir Quality Analysis

In this example, nine log measurements were obtained from a verticalwell and used to establish a continuous log of reservoir quality (RQ).The sample logs include gamma ray (GR, gAPI), deep resistivity (AT90,ohm*m), bulk density (RHOZ, g/cm3), thermal neutron porosity (NPHI,v/v), carbon weight fraction (WCAR, lbf/lbf), clay weight fraction(WCLA, lbf/lbf), pyrite weight fraction (WPYR, lbf/lbf),quartz-feldspar-mica weight fraction (WQFM, lbf/lbf), and total organiccarbon weight fraction (WSM, lbf/lbf). The studied interval is 500 feetin measured depth. Other logs which may be used for reservoir qualityare neutron (TNPH); spectroscopy mineralogy logs: Anhydrite (WANH),Calcite (WCLC), Dolomite (WDOL), Evaporite (WDOL), Pyrite (WPYR), MatrixGrain Density (RHGE); and NMR: NMR porosity (MRP), permeability (KSDR)

Generating Correlation Matrix

With particular respect to FIG. 2, a pair-wise correlation matrix wascalculated and generated as a map for all available wellbore logs as avisualization tool to depict the correlation between the values in pairsof the respective logs. In the correlation matrix, the bar legend showsthe correlation among the logs from negatively correlated values (−1) topositively correlated values (+1). The correlation matrix was used toseparate the logs into two groups based on the strength of thecorrelation between the groups. Based on common petrophysicalunderstanding about reservoirs, the logs in the first group 202 areexamples of logs that can be good indicators of reservoir quality andwere used to generate a continuous log of reservoir quality (RQ): GR,AT90, RHOZ, and NPHI. The second group 204, is directed to logs ofmineral concentrations that were used to generate a continuous log ofcompletion quality (CQ), which indicates regions of the wellbore thatmay be good targets for stimulation and completion operations: WCLA,WPYR, WQFM, and WSM. In addition to the mineralogy logs, sonic log andother logs relating to rock geomechanics properties could be used. Inthis example, the groups selected were representative of the respectivequality logs RQ or CQ, however, highly redundant logs could be removedand remaining logs can be regrouped and reordered to form a newcorrelation matrix depending on the job requirements. The correlationsbetween the logs for RQ and logs for CQ lie inside the dashed box 206.

Principal Component Analysis

The next step is to pick the groups of logs for either RQ or CQcomputation. As an illustration of RQ computation, we chose four logs(GR, AT90, RHOZ, NPHI). Principal component analysis (PCA) is applied todetermine the principal component (PC) vectors that explain most of thetotal variance in the dataset. PCA is a technique that is widely usedfor applications such as dimension reduction, data compression, featureextraction, and data visualization.

Before applying PCA, a positive sign “+” or a negative sign “−” needs tobe specified for each individual log measurement to indicate whether thecorresponding measurement contributes to a composite index positively ornegatively. In this example, three of the four logs have a positive sign“+”, while the density log (RHOZ) has a negative sign “−”. The physicalreason for the negative correlation of density to RQ is that hydrocarbonin pore space of a rock is less dense than the surrounding rock andreduces the rock bulk density. Negatively correlated logs are flipped bysubtracting the original log measurements from their maximum values.

Next, all log measurements are converted into dimensionless valuesthrough Z-score transformation. This is required since different typesof logs commonly have different units and different ranges ofmeasurement. Z-score transformation of a data vector x is defined in Eq.(1), in which the mean is subtracted from the data vector and thendivided by the standard deviation of the data. It is worth noting thatfor resistivity log AT90, a logarithmic transformation is applied firstbefore Z-score transformation to reduce skewness in the resistivity datameasurements.

$\begin{matrix}{Z = \frac{x - {{mean}\mspace{14mu}(x)}}{{std}(x)}} & (1)\end{matrix}$

PCA involves evaluating the correlation matrix R of the data set first,which are defined in Eq. (2), where zn (n=1, N) is the Z-scoretransformed log using equation (1) and each contains L measurements,which usually corresponds to the total samples in studied depthinterval.

$\begin{matrix}{R = {\frac{1}{N}{\sum\limits_{n - 1}^{N}{z_{n}z_{n}^{T}}}}} & (2)\end{matrix}$

The dimension of the correlation matrix R is N×N. In our example, N=4and L is the total measurement data points, and z_(n) ^(T) is thetranspose of z_(n).

Next, PCA is performed to find the M(M<N) eigenvector u_(i) of Rcorresponding to the M largest eigenvalues λ_(i) by Eq. (3), whereeigenvectors u_(i) are chosen to be orthogonal with unit length.Ru _(i)=λ_(i) u _(i)  (3)

It can be observed from equation (3) that searching for the eigenvectorsis equivalent to covariance matrix diagonalization, which amounts torotating the original N-dimensional data space to a new system with allthe eigenvectors u_(i) as the basis. Along each axis identified by oneeigenvector, the data spread or variation is measured by its eigenvalue,typically ranking in descending order: λ₁≤λ₂≤ . . . ≤λ_(N) andsatisfying the condition set forth in Eq. (4), where σ² represents thetotal variance of the data.λ₁+λ₂+ . . . +λ_(N)=σ²  (4)

With particular respect to FIG. 3.1, the correlation map for fourselected logs is shown, while the corresponding correlation matrix isshown in FIG. 3.2, and the first three principal components that explainmore than 90% of the total variance of the data are shown in FIG. 3.3.In this example, the first three PCs explain more than 90% of the totalvariance, thus, only the first three PCs are retained for furtheranalysis. However, more or fewer PCs may be used depending on the degreeof accuracy needed for the particular application.

Next, let U=(u₁, u₂, . . . u_(n)) represent the N eigenvectors, andA=diag(λ₁, λ₂, . . . , λ_(N)) is the diagonal matrix of N eigenvalues.The relationship may be described by (5), where U has dimension of N×Nand is also an orthogonal matrix satisfying UU^(T)=I, with I being a N×Nidentity matrix. U^(T) is the transpose of the matrix U.R=UAU ^(T)  (5)

Once eigenvectors u_(i) (i=1, . . . , N) are obtained, principalcomponent (PC) vectors are computed by multiplying eigenvectors by thedata matrix as shown in Eq. (6), where D is a L×N data matrix; U is aN×N matrix of the eigenvectors; resulting V is a L×N matrix with columnsbeing the PC vectors.V=DU  (6)

The values in the n-th column of the eigenvector matrix U is called theloading of n-th log measurement (variable) (n=1, . . . , N) on all theprincipal components. If the first M (M<N) eigenvectors are retained, Ubecomes N×M matrix, data matrix D stays the same and V will be L×Mmatrix, leading to only the first M PC vectors considered. Largerloadings, which could be either positive or negative, indicate that thecorresponding log measurements are more significant than others in theexplanation of the total data variation.

With respect to FIG. 4.1, the first two principal components for thefour log data measurements (GR, AT90, RHOZ, NPHI) is shown in 2D space,and their corresponding loading vectors is shown, while FIG. 4.2 showsthe first three principal components in 3D space. The 3D plot allowsusers to visualize the magnitude and sign of each variable's (logmeasurement in our example) contribution to the first two or threeprincipal components, and how each observation is represented in termsof those components. The plot scales the principal components so thatthey fit on the plot: It divides each principal component by the maximumabsolute value of all the components, and multiplies by the maximumvalue of all the eigenvectors. The scaled data are used for furtheranalysis

In FIGS. 4.1 and 4.2, prefix “˜” before the bulk density log name “RHOZ”indicates that RHOZ contributes to the RQ negatively, hence itscomplementary value is considered in the PCA analysis. In PCA, thesignificance of each individual log measurement can be measured by thelength of its corresponding loading vector, while the redundancy amongall the logs can be measured by their relative closeness in PC space,which may be determined by the angles between any pair of loadingvectors.

Weighting Factor Derivation

Weighting factors for each of the wellbore logs are derived by removinglog redundancy. Highly correlated logs are given smaller weights, whiledivergent logs are assigned higher weights. While linear PCA is used inthese examples for data reduction, there are other methods that could besubstituted for linear PCA, including non-linear or kernel PCA, or anyother suitable dimension reduction tools.

In this example, the PC loading vector displays in FIGS. 4.1 and 4.2suggest that the resistivity log (AT90) is separated from the other logs(GR, RHOZ, and NPHI), which is an indicator that AT90 exhibits more datavariation and should be weighted more when combining the logs to inferthe reservoir quality (RQ). On the other hand, the other three logsshould share the weights due to the redundancy exhibited in the PCAanalysis.

Kriging Analysis of PC Loading Vectors

After PCA analysis of all the log measurements and obtaining theirloading vectors, a Kriging Estimator is applied on the loading vectorsin the PC space in FIGS. 4.1 and 4.2. The Kriging estimator serves as aspatial interpolator by linearly combining spatial data at knownlocations to infer the studied property at any unknown location throughoptimal determination of the weighting factors as shown in Eq. (7),where Z_(i) (i=1, 2, . . . , N) represents spatially correlated randomvariables at N known locations; and Z* is estimated variable by a linearcombination of all the random variable with the corresponding weighingfactor ω_(i).Z*=Σ _(i=1) ^(N)ω_(i) Z _(i)  (7)

The optimum weighting factors ω_(i) (i=1, 2, . . . , N) are then chosento minimize the expected error in a least square sense as shown in Eq.(8), where Z is the true value at an unknown location but it is notavailable and will be estimated; E represents the expectation of thesquared error between the estimator and the true value; and σ_(SK) ² isthe resulting error or Kriging variance.σ_(SK) ²=min E(Z−Z*)²  (8)

Further mathematical derivation of equation (8) leads to the followingKriging equation shown in Eq. 9, where the Kriging matrix at the lefthand consists of pair-wise covariance C_(ij) (i, j=1, 2, . . . , N)between two random variables Z_(i) and Z_(j).

$\begin{matrix}{{\begin{bmatrix}C_{11} & \ldots & C_{1\; N} \\\vdots & \ddots & \vdots \\C_{N\; 1} & \ldots & C_{NN}\end{bmatrix}\begin{pmatrix}\omega_{1} \\\vdots \\\omega_{N}\end{pmatrix}} = \begin{pmatrix}C_{01} \\\vdots \\C_{0N}\end{pmatrix}} & (9)\end{matrix}$

The column vector at the right hand side (C_(0j)) (j=1, 2, . . . , N)measures the dependence between each variable at the known locationswith the unknown. The Kriging matrix itself is a measure of the dataredundancy, such that more spatially isolated data are assigned largerKriging weights while spatially clustered data share the weight.

To solve a Kriging system, a covariance function is usually required todetermine the correlation matrix and the correlation vector in equation(9). The covariance function determines the spatial anisotropy of thedata in 3D once three orthogonal main directions are specified and thecorresponding correlation range along each direction is provided. Inpractice in Kriging estimation, an experimental covariance from knowndata is computed and then fit by parametric covariance functions togenerate a theoretical covariance function model for solving the Krigingsystem above. Once the Kriging weights in equation (9) are determined,Kriging estimation can be computed by plugging the weights into equation(7).

Continuing the above example, Kriging theory is applied to the loadingvectors in principal component space as shown in FIGS. 4.1 and 4.2. Theestimated location is set to be at the origin of the coordinate system(0, 0, 0). Because the weighting factors in Kriging system aredetermined by the proximity of each datum to the estimated location, thedata points (the four loading vectors) are converted by their inversedistances to the origin of the coordinate, multiplied by the summationof lengths of all loading vectors. This reciprocal transformation doesnot change the angles between any two loading vectors, but reverses thelength of each loading vector, such that loading vectors with longerlengths are shortened. As a result, the loading points are closer to theorigin in the reciprocal transformation and, consequently, theircorresponding logs receive more weight.

In the next step, a covariance model required by Kriging estimator isused to measure the data dependency and/or redundancy in the reciprocalspace of PC loadings. In one or more embodiments, a covariance model maybe chosen such that:

(i) The three major directions are chosen as the three principalcomponent directions of the data by PCA analysis, and the three majorcorrelation axes are chosen as: the third PC direction, the second PCdirection, and the first PC direction in order, because of reciprocaltransformation of PC loading vectors as discussed above.

(ii) The three correlation ranges in the three major directions are setas shown in Eq. (10), where {circumflex over (σ)}²=(λ₁+λ₂+λ₃) representsthe total variance explained by the first three principal components andλ_(i) (i=1, 2, 3) are the three eigenvalues discussed above. It is seenthat R₁≥R₂≥R₃ because λ₁≥λ₂≥λ₃.

$\begin{matrix}{{R_{1} = \frac{{\hat{\sigma}}^{2}}{\lambda_{1}}},{R_{2} = {R_{1}\frac{\lambda_{2}}{\lambda_{1}}}},{R_{3} = {R_{1}\frac{\lambda_{3}}{\lambda_{1}}}}} & (10)\end{matrix}$

(iii) The covariance function is proposed as an exponential functionshown in Eq. (11).

$\begin{matrix}{{C(h)} = {{\hat{\sigma}}^{2}*e^{({- \frac{h}{3.0}})}}} & (11)\end{matrix}$

The correlation range is set to 3.0 in Eq. (11), which represents threetimes the standard deviation of a random normal Gaussian variable.

Generation of Reservoir Quality Index

In the next step, an RQ index is generated by the linear combination ofthe four wellbore logs. Kriging weights are calculated by solving thelinear system in equation (9) using the covariance model proposed inequations (10) and (11) in reciprocal space of loading vectors. All theweights are normalized so they sum to 1.0.

Continuing the above example, four weighting factors corresponding tothe four normalized logs are generated, and multiplied by the fournormalized logs. The input log measurements are each normalized bysubtracting each value by the minimum value from its original logmeasurement for each log and divided by its range as shown in Eq. (12).

$\begin{matrix}{x_{normalized} = \frac{x - x_{m\; i\; n}}{x_{{ma}\; x} - x_{m\; i\; n}}} & (12)\end{matrix}$

This normalization constrains each log between 0 and 1. The finalwellbore quality index generated by applying the Kriging weights to thenormalized logs, which produces a unitless composite measure between 0and 1 with higher values representing an increase in the studied quality(reservoir quality in this example).

With respect to FIG. 5, a continuous RQ index 510 is generated bylinearly combining four well log measurements: gamma ray (GR) 502, deepresistivity (AT90) 504, bulk rock density (RHOZ) 506, and thermalneutron porosity (NPHI) 508. Linear combination is performed byweighting the wellbore logs according to the respective weightscalculated from a Kriging estimator: 0.18 for GR, 0.35 for AT90, −0.22for RHOZ, and 0.24 for NPHI. A positive weighting factor (GR, AT90, andNPHI) indicates that the log measurement is positively correlated withRQ, while a negative weighting factor (RHOZ) correlates negatively withRQ. The magnitudes of the weighting factors suggest that the inductionAT90 has the most impact on the RQ while the GR log has the leastinfluence. In this case, lower bulk density, RHOZ, indicates higher RQ,because hydrocarbon in pores are less dense than rock mineral grains.

Continuous wellbore quality logs in accordance with the presentdisclosure may also be modified with a user-defined threshold, which mayfacilitate the identification of regions of interest. In this example,the RQ index generated can be used to flag reservoir pay zones bysetting a user-defined RQ threshold. In one or more embodiments, theselection of a threshold value may be an iterative, user-definedprocess. By adjusting the threshold value, intervals can be determinedand displayed that correspond to the zone with RQ above the threshold(shaded regions 518). Some criteria such as a specified 70% pay zonecould be used by the user to find the respective threshold.

An RQ index may be used, for example, to predict pay intervals bysetting a threshold of reservoir quality to flag regions of interestthat may be economically viable. This thresholding can be doneinteractively by the user by applying a binary threshold cutoff toidentify an interval of interest (a pay flag in this example, shadedregions 518) above the threshold value. With respect to FIG. 5, RQ index510 is modified by the addition of threshold 514 of 0.52, which may bevisually indicated by shaded intervals of interest 518. Further,horizontal lines 514 across all logs may be used to represent thecorresponding pay zone boundaries. Thresholding could also be used togenerate other forms of reservoir information, including pay flagproportion for a measured wellbore interval or within a formationinterval of interest.

Wellbore quality index generation may be automated with weightingfactors computed from PCA analysis of the log measurements, followed byKriging analysis applied to the corresponding loading vectors. In someembodiments, sign selection of the wellbore logs may be used to trainthe algorithm to calculate the optimal weights. The final continuousquality index then aids selection of intervals of interest. For example,as observed in RQ index 510 the lower interval from 6760 feet to 6990feet contains better reservoir quality than the upper interval of thestudied reservoir log.

It is worth noting that within the formation interval from 6500 to 6650feet, each of the three logs AT90, RHOZ and NPHI contains both theirminimum and maximum values of the corresponding log across the entireinterval from 6440 to 6990 (approximately the upper half of thedisplayed interval). If one were to use conventional binary thresholdingtechniques on each individual wellbore log, this would identify theentire interval as good quality. However, by using methods in accordancewith the present disclosure the RQ index indicates that the sameinterval is low RQ because of the automatic removal of the logredundancy and optimal weighting factor computation.

In instances where regions of interest such as pay flags are provided,optimal thresholding of the continuous RQ index may be computed bysupervised machine learning methods by minimizing the misclassificationrate by adjusting the cutoff value.

With respect to FIG. 6, an example is shown in which a pay flaginterpretation 612 is available. RQ index 610 is generated by linearlycombining four well log measurements: gamma ray (GR) 602, deepresistivity (AT90) 604, bulk rock density (RHOZ) 606, and thermalneutron porosity (NPHI) 608. In log 612, pay intervals determined by asupervised classification method are indicated by shaded region. Optimalthresholding value 0.46 (dashed line 614) was determined by minimummisclassification value determined by the classification algorithm usingthe continuous RQ log as input compared with the supplied knownexamples. Pay labels in 612 may be obtained, for example, bypetrophysicists using a potentially more extensive suite of measurementsfrom well logs and possibly core or cuttings samples, leading tocomprehensive, data-rich interpretations. Such comprehensive analysesand interpretations are common in key wells or pilot wells in ahydrocarbon field study. These results are commonly propagated to lessdata-rich wells in a field.

Example 2: Generation of CQ Index

In the next example, the workflow described above is applied to computecompletion index from a suite of wellbore logs indicating variousmineral concentrations and other factors that impact the success ofvarious completion techniques. In addition to reservoir qualityevaluation, knowing the completion quality is critical for hydraulicfracturing in low porosity reservoirs. Determination of both good RQ andgood CQ helps identification of sweet spots in developing and productionof unconventional reservoirs. Four mineral logs are selected in thisexample for CQ computation: clay (WLCA), pyrite (WPYR),quartz-feldspar-mica (WQFM), total organic carbon (WSM), and all aremeasured in weight fraction. Other logs which may be used for CQcomputation include: Gamma Ray (GR), Resistivity (AT90), Density (RHOZ),Neutron (TNPH or NPHI), Compressional Slowness (DT), Fast Shear(DTSM_FAST); and from petrophysical evaluation: quartz, dolomite, clay(VCL), calcite, evaporite, anhydrite, Matrix Grain Density (RHGE) andtotal organic carbon (TOC).

With respect to FIG. 7.1, a correlation matrix is shown for the fourmineral logs, with the nominal values shown in FIG. 7.2. As with the RQindex example, the correlation matrix is explained by the first threecomponents as shown in FIG. 7.3. Weak positive correlations among allthe four logs can be observed except for a stronger positive correlation(=0.76) between WQFM and WSM.

With respect to FIG. 8.1, the loading vectors by PCA analysis on thefour log measurements are displayed in PC space in 2D, while the loadingvectors are shown in 3D in FIG. 8.2. All logs are considered tocontribute to CQ negatively except for WQFM, because morequartz-feldspar-mica concentration in a rock tends to increase itsbrittleness and improving the likelihood of successful completion. Theloading vector display in FIGS. 8.1 and 8.2 demonstrate that WQFM andWSM are slightly separated from the other two logs, which implies thatheavier weighting factors may be assigned to these two logs in CQcomputation.

With respect to FIG. 9, all four mineral logs clay (WLCA) 902, pyrite(WPYR) 904, quartz-feldspar-mica (WQFM) 906, total organic carbon (WSM)908, and the resulting CQ index 910 are displayed. The weighting factorsfor the logs are −0.21 (WCLA), −0.22 (WPYR), 0.3 (WQFM) and −0.27 (WSM).All logs contribute negatively to the completion quality of thereservoir, with the exception of a positive weighting to WQFM, whichindicates that a higher value increases likelihood of completion asresult of more brittle rock in the reservoir. Per the computed weightingfactors, the influence of each individual mineral on the CQ can beranked as WQFM>WSM>WPYR>WCLA.

Note that the computed CQ 910 in FIG. 9 has more variation than the RQindex 610 in FIG. 6, and the CQ intervals seem to be independent of RQ.The upper (lower MD) layer contains poorer RQ and better CQ, while thelower layer (higher MD) contains better RQ and poor CQ. This suggeststhat the selection of sweet spots (good RQ+good CQ) may require atrade-off between the two computed quality indices.

In one or more embodiments, composite quality indices may be generatedfrom multiple quality indices. For example, a composite quality index(QI) curve can be defined as a product of the RQ and CQ, QI=RQ*CQ, orweighted using the weights assigned by interpreters. The resultingcontinuous QI index could then be modified by the thresholding processdiscussed above, leading to the identification of “sweet spot” intervalsto guide the engineering completion. Using RQ, CQ or QI computed forsingle wells, interpolating it in 3D using any spatial interpolatorssuch as Geostatistics tools can generate spatial 3D models of RQ, CQ orQI for more accurate reservoir delineation and characterization.

If the pay flag is provided in one well, the optimal thresholding valueof continuous RQ/CQ index can be determined by supervised classificationmethods. Later, the same threshold value can be applied to cutoff RQ/CQindices curves for other wells to get pay/completable flags if their logcharacteristics are similar and the corresponding weighting factors arecomparable.

RQ, CQ or a composite QI can be further used to perform interpolation ormapping of RQ, CQ, or QI in 3D. The computed RQ, CQ or QI at wells canbe considered as known data and interpolation tools, such asgeostatistical methods, can be used to build 3D models to eitherestimate or simulate the RQ, CQ or QI values between wells. Theresulting RQ, CQ, QI predictions in 3-dimensional space can helpoperators to select locations for optimal exploration or productiontarget areas or for infill drilling.

Applications

While the methods in accordance with the present disclosure may beadapted to data analysis in the wellbore context, it is also envisionedthat the method may be applied more broadly as a data analytics toolused to combine data from different sources to generate singlecontinuous indices for interpretation and decision-making.

In one or more embodiments, methods in accordance with the presentdisclosure may be automated or partially automated. In some embodiments,a user-guided iterative interpretation process can be conducted bythresholding the continuous quality index to generate quality flags toassist optimal reservoir development, or guide the decision-makingprocess. In one or more embodiments, methods may be applied to anydeviated or horizontal wells provided a set of log measurements areavailable and registered to their position in a well or in space, eitherfor real-time analysis of drilling measurements, geosteering, orpost-drill mode measurement analysis.

Computing System

Embodiments of the present disclosure may be implemented on a computingsystem. Any combination of mobile, desktop, server, embedded, or othertypes of hardware may be used. For example, as shown in FIG. 10, thecomputing system (1000) may include one or more computer processor(s)(1002), associated memory (1004) (e.g., random access memory (RAM),cache memory, flash memory, etc.), one or more storage device(s) (1006)(e.g., a hard disk, an optical drive such as a compact disk (CD) driveor digital versatile disk (DVD) drive, a flash memory stick, etc.), andnumerous other elements and functionalities. The computer processor(s)(1002) may be an integrated circuit for processing instructions. Forexample, the computer processor(s) may be one or more cores, ormicro-cores of a processor configured to perform methods describedabove, including normalizing two or more wellbore logs obtained from theoutput of two or more wellbore tool surveys of a wellbore in a formationof interest; inputting two or more wellbore logs into a correlationmatrix; assigning each of the two or more wellbore logs a positive ornegative value based on the impact on a selected wellbore quality;performing a principal component analysis of the two or more wellborelogs to obtain one or more loading vectors; computing weighting factorsfor each of the two or more wellbore logs from the one or more loadingvectors; and generating a quality index by linearly combining the two ormore wellbore logs using the computed weighting factors.

The computing system (1000) may also include one or more input device(s)(1010), such as a touchscreen, keyboard, mouse, microphone, touchpad,electronic pen, or any other type of input device. Further, thecomputing system (1000) may include one or more output device(s) (1008),such as a screen (e.g., a liquid crystal display (LCD), a plasmadisplay, touchscreen, cathode ray tube (CRT) monitor, projector, orother display device), a printer, external storage, or any other outputdevice. One or more of the output device(s) may be the same or differentfrom the input device(s). The computing system (1000) may be connectedto a network (1012) (e.g., a local area network (LAN), a wide areanetwork (WAN) such as the Internet, mobile network, or any other type ofnetwork) via a network interface connection (not shown). The input andoutput device(s) may be locally or remotely (e.g., via the network(1012)) connected to the computer processor(s) (1002), memory (1004),and storage device(s) (1006). Many different types of computing systemsexist, and the aforementioned input and output device(s) may take otherforms.

Software instructions in the form of computer readable program code toperform embodiments of the disclosure may be stored, in whole or inpart, temporarily or permanently, on a non-transitory computer readablemedium such as a CD, DVD, storage device, a diskette, a tape, flashmemory, physical memory, or any other computer readable storage medium.Specifically, the software instructions may correspond to computerreadable program code that when executed by a processor(s), isconfigured to perform embodiments of the disclosure. Further, one ormore elements of the aforementioned computing system (1000) may belocated at a remote location and connected to the other elements over anetwork (1012).

Further, embodiments of the disclosure may be implemented on adistributed system having a plurality of nodes, where each portion ofthe disclosure may be located on a different node within the distributedsystem. In one embodiment of the disclosure, the node corresponds to adistinct computing device. Alternatively, the node may correspond to acomputer processor with associated physical memory. The node mayalternatively correspond to a computer processor or micro-core of acomputer processor with shared memory and/or resources.

Although only a few examples have been described in detail above, thoseskilled in the art will readily appreciate that many modifications arepossible in the examples without materially departing from this subjectdisclosure. Accordingly, all such modifications are intended to beincluded within the scope of this disclosure as defined in the followingclaims. In the claims, means-plus-function clauses are intended to coverthe structures described herein as performing the recited function andnot only structural equivalents, but also equivalent structures. Thus,although a nail and a screw may not be structural equivalents in that anail employs a cylindrical surface to secure wooden parts together,whereas a screw employs a helical surface, in the environment offastening wooden parts, a nail and a screw may be equivalent structures.It is the express intention of the applicant not to invoke 35 U.S.C. §112 (f) for any limitations of any of the claims herein, except forthose in which the claim expressly uses the words ‘means for’ togetherwith an associated function.

What is claimed:
 1. A method, comprising: normalizing two or morewellbore logs obtained from the output of two or more wellbore toolsurveys of a wellbore in a formation of interest to provide two or morenormalized wellbore logs; inputting the two or more normalized wellborelogs into a correlation matrix; assigning each of the two or morenormalized wellbore logs a positive or negative value based on theimpact on a selected wellbore quality; performing a principal componentanalysis of the two or more normalized wellbore logs to obtain one ormore loading vectors; computing weighting factors for each of the two ormore normalized wellbore logs from the one or more loading vectors;generating a quality index by linearly combining the two or morenormalized wellbore logs using the computed weighting factors; inputtinga user-defined threshold for the selected quality being indexed, whereinthe quality index is a reservoir quality log; designating one or morewellbore intervals in a reservoir quality log as pay regions based onthe user-defined threshold; and transitioning the wellbore to productionand producing hydrocarbons from the one or more wellbore intervalsdesignated as pay regions.
 2. The method of claim 1, wherein the two ormore wellbore logs comprise at least gamma ray, deep resistivity, bulkrock density, and thermal neutron porosity.
 3. The method of claim 1,wherein the quality index further comprises a completion quality log,and wherein the method further comprises: designating one or moreregions in the completion quality log as completion targets.
 4. Themethod of claim 3, further comprising: completing one or more regionsdesignated as completion targets.
 5. The method of claim 3, wherein thetwo or more wellbore logs comprise at least clay, pyrite,quartz-feldspar-mica, and total organic carbon.
 6. The method of claim1, wherein computing weighting factors for each of the two or morenormalized wellbore logs comprises analyzing each of the normalizedwellbore logs with a Kriging estimator that analyzes the loading vectorsof the normalized wellbore logs from the principal component analysis.7. The method of claim 1, further comprising: performing interpolationor mapping of the quality index in 3D.
 8. The method of claim 1, whereinthe user-defined threshold is optimized to minimize themisclassification rate of one or more wellbore intervals in the qualitylog.
 9. The method of claim 8, wherein the optimized threshold isapplied to quality logs obtained from other wells.
 10. The method ofclaim 1, wherein the principal component analysis comprises kernelprincipal component analysis.
 11. The method of claim 1, whereinperforming the principal component analysis comprises plotting principalcomponent analysis results in 2D or 3D, and wherein the plotted data isused to generate the loading vectors.
 12. The method of claim 1, whereinthe two or more wellbore logs comprises at least four wellbore logsselected from a group consisting of: gamma ray, deep resistivity, bulkdensity, thermal neutron porosity, carbon weight fraction, clay weightfraction, pyrite weight fraction, quartz-feldspar-mica weight fraction,total organic carbon weight fraction, neutron, spectroscopy mineralogy,anhydrite, calcite, dolomite, evaporite, matrix grain density, NMRporosity, and permeability.
 13. The method of claim 3, wherein the twoor more wellbore logs comprises at least four wellbore logs selectedfrom a group consisting of: clay, pyrite, quartz-feldspar-mica, totalorganic carbon, gamma ray, resistivity, density, neutron, compressionalslowness, fast shear, quartz, dolomite, clay, calcite, evaporite,anhydrite, and matrix grain density.
 14. A method, comprising:normalizing two or more wellbore logs obtained from the output of two ormore wellbore tool surveys of a wellbore in a formation of interest toprovide two or more normalized wellbore logs; inputting the two or morenormalized wellbore logs into a correlation matrix; assigning each ofthe two or more normalized wellbore logs a positive or negative signbased on the impact on a selected wellbore quality; performing aprincipal component analysis of the two or more normalized wellbore logsto obtain one or more loading vectors; computing weighting factors foreach of the two or more normalized wellbore logs from the one or moreloading vectors; generating a reservoir quality log by linearlycombining the two or more normalized wellbore logs using the computedweighting factors; generating a completion quality log by linearlycombining the two or more normalized wellbore logs using the computedweighting factors; preparing a composite reservoir quality index fromthe product of the reservoir quality log and the completion quality log;inputting a user-defined threshold for the composite reservoir qualitylog; designating one or more wellbore intervals in a composite reservoirquality log as regions of interest based on the user-defined threshold;and transitioning the wellbore to production or completion and producinghydrocarbons from or completing the one or more wellbore intervalsdesignated as regions of interest.
 15. The method of claim 14, whereinthe two or more wellbore logs are selected from a group consisting of:gamma ray, deep resistivity, bulk rock density, thermal neutronporosity, clay, pyrite, quartz-feldspar-mica, and total organic carbon.16. The method of claim 14, wherein computing weighting factors for eachof the two or more normalized wellbore logs comprises analyzing each ofthe normalized wellbore logs with a Kriging estimator that analyzes theloading vectors of the normalized wellbore logs from the principalcomponent analysis.
 17. The method of claim 14, further comprisingperforming interpolation or mapping of the composite reservoir qualityindex in 3D.
 18. The method of claim 14, wherein the user-definedthreshold is optimized to minimize the misclassification rate of one ormore wellbore intervals in the composite reservoir quality log.
 19. Themethod of claim 14, wherein the principal component analysis compriseskernel principal component analysis.
 20. The method of claim 14, whereinperforming the principal component analysis comprises plotting principalcomponent analysis results in 2D or 3D, and wherein the plotted data isused to generate the loading vectors.